计量经济学
ARCH模型
波动性(金融)
期权估价
杠杆(统计)
经济
高斯分布
估价(财务)
贝叶斯概率
库存(枪支)
数学
统计
财务
工程类
物理
机械工程
量子力学
作者
Fayan Zhu,Michele Leonardo Bianchi,Young S. Kim,Frank J. Fabozzi,WU Heng-yu
出处
期刊:Studies in Nonlinear Dynamics and Econometrics
[De Gruyter]
日期:2020-06-01
卷期号:25 (3): 35-62
标识
DOI:10.1515/snde-2019-0088
摘要
Abstract This paper studies the option valuation problem of non-Gaussian and asymmetric GARCH models from a state-space structure perspective. Assuming innovations following an infinitely divisible distribution, we apply different estimation methods including filtering and learning approaches. We then investigate the performance in pricing S&P 500 index short-term options after obtaining a proper change of measure. We find that the sequential Bayesian learning approach (SBLA) significantly and robustly decreases the option pricing errors. Our theoretical and empirical findings also suggest that, when stock returns are non-Gaussian distributed, their innovations under the risk-neutral measure may present more non-normality, exhibit higher volatility, and have a stronger leverage effect than under the physical measure.
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